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demacdolincoln/slides.ipynb

Created August 17, 2016 23:19
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slides.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"\n",
"# Processando dados da natureza\n",
"\n",
"<small>Lincoln de Macedo</small>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## No final das contas do que estamos tratamos mesmo???"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"<h1 style=\"text-align:center\">plotar gráficos</h1>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Falarei quase que o tempo todo do Pandas (e de uma de suas derivações)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import geopandas as gpd\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Antes de iniciar os códigos uma pergunta: onde conseguir os dados???"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"os que usarei se encontram em:\n",
"\n",
"* http://dados.gov.br/dataset/destinacao-do-lixo-domiciliar\n",
"* http://data.globalforestwatch.org/datasets/ (http://bit.ly/2aYTppe)\n",
"* https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Variação de temperatura"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"data_clima = pd.read_csv(\"files/GlobalLandTemperaturesByCountry.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f06dc4ee1d0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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G+Lp6mNlfzewFM+tmZo+Y2RdmttDMemYow3E1Ht8DnjOzYzNZ3mZ2YZXnnczs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\nawAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f06dc4b9898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l10_brasil = data_clima[data_clima.Country == \"Brazil\"][:10]\n",
"l10_brasil.plot.bar()\n",
"# l10_brasil.plot(grid=True,figsize=(15,3))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Venda de agrotóxicos 2014"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"agrotx = pd.read_excel(\"files/vendasingredientesativosuf2014.xlsx\",header=2,skiprows=[79,80,81,82,83,84,85,86])\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"agrotx.RO.plot.bar?"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ingrediente_Ativo</th>\n",
" <th>RO</th>\n",
" <th>AC</th>\n",
" <th>AM</th>\n",
" <th>RR</th>\n",
" <th>PA</th>\n",
" <th>AP</th>\n",
" <th>TO</th>\n",
" <th>MA</th>\n",
" <th>PI</th>\n",
" <th>...</th>\n",
" <th>SP</th>\n",
" <th>PR</th>\n",
" <th>SC</th>\n",
" <th>RS</th>\n",
" <th>MS</th>\n",
" <th>MT</th>\n",
" <th>GO</th>\n",
" <th>DF</th>\n",
" <th>Vendas_sem_Definição_de_UF</th>\n",
" <th>VENDAS_TOTAIS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2,4-D</td>\n",
" <td>1140.383233</td>\n",
" <td>377.557023</td>\n",
" <td>14.567980</td>\n",
" <td>324.979547</td>\n",
" <td>1373.359321</td>\n",
" <td>0.901872</td>\n",
" <td>560.202039</td>\n",
" <td>784.464823</td>\n",
" <td>450.393610</td>\n",
" <td>...</td>\n",
" <td>3691.762568</td>\n",
" <td>5677.961012</td>\n",
" <td>356.125292</td>\n",
" <td>4477.514234</td>\n",
" <td>2994.935636</td>\n",
" <td>5198.975337</td>\n",
" <td>2085.396309</td>\n",
" <td>10.325236</td>\n",
" <td>1508.477275</td>\n",
" <td>36513.548652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>abamectina</td>\n",
" <td>0.016466</td>\n",
" <td>0.000000</td>\n",
" <td>0.002196</td>\n",
" <td>0.109152</td>\n",
" <td>0.554362</td>\n",
" <td>0.000000</td>\n",
" <td>1.187555</td>\n",
" <td>1.281737</td>\n",
" <td>4.605776</td>\n",
" <td>...</td>\n",
" <td>28.248814</td>\n",
" <td>14.424588</td>\n",
" <td>1.416323</td>\n",
" <td>8.694401</td>\n",
" <td>2.962124</td>\n",
" <td>18.907690</td>\n",
" <td>9.472299</td>\n",
" <td>0.452963</td>\n",
" <td>-0.055478</td>\n",
" <td>130.383601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>acefato</td>\n",
" <td>104.320000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.690000</td>\n",
" <td>46.221750</td>\n",
" <td>0.138750</td>\n",
" <td>259.171500</td>\n",
" <td>192.349000</td>\n",
" <td>171.719400</td>\n",
" <td>...</td>\n",
" <td>3085.617455</td>\n",
" <td>2976.129350</td>\n",
" <td>179.979250</td>\n",
" <td>2295.128250</td>\n",
" <td>1443.432700</td>\n",
" <td>7891.394250</td>\n",
" <td>2790.259550</td>\n",
" <td>41.304000</td>\n",
" <td>2550.045000</td>\n",
" <td>26190.520005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>acetamiprido</td>\n",
" <td>1.045830</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.245710</td>\n",
" <td>2.449020</td>\n",
" <td>0.000000</td>\n",
" <td>7.881500</td>\n",
" <td>11.607990</td>\n",
" <td>13.211570</td>\n",
" <td>...</td>\n",
" <td>13.013410</td>\n",
" <td>54.699990</td>\n",
" <td>3.955820</td>\n",
" <td>32.862230</td>\n",
" <td>27.007250</td>\n",
" <td>419.890590</td>\n",
" <td>46.441680</td>\n",
" <td>2.034830</td>\n",
" <td>0.000000</td>\n",
" <td>822.148690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>alacloro</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" Ingrediente_Ativo RO AC AM RR \\\n",
"0 2,4-D 1140.383233 377.557023 14.567980 324.979547 \n",
"1 abamectina 0.016466 0.000000 0.002196 0.109152 \n",
"2 acefato 104.320000 0.000000 0.000000 2.690000 \n",
"3 acetamiprido 1.045830 0.000000 0.000000 0.245710 \n",
"4 alacloro 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
" PA AP TO MA PI ... \\\n",
"0 1373.359321 0.901872 560.202039 784.464823 450.393610 ... \n",
"1 0.554362 0.000000 1.187555 1.281737 4.605776 ... \n",
"2 46.221750 0.138750 259.171500 192.349000 171.719400 ... \n",
"3 2.449020 0.000000 7.881500 11.607990 13.211570 ... \n",
"4 0.000000 0.000000 0.000000 0.000000 0.000000 ... \n",
"\n",
" SP PR SC RS MS \\\n",
"0 3691.762568 5677.961012 356.125292 4477.514234 2994.935636 \n",
"1 28.248814 14.424588 1.416323 8.694401 2.962124 \n",
"2 3085.617455 2976.129350 179.979250 2295.128250 1443.432700 \n",
"3 13.013410 54.699990 3.955820 32.862230 27.007250 \n",
"4 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
" MT GO DF Vendas_sem_Definição_de_UF \\\n",
"0 5198.975337 2085.396309 10.325236 1508.477275 \n",
"1 18.907690 9.472299 0.452963 -0.055478 \n",
"2 7891.394250 2790.259550 41.304000 2550.045000 \n",
"3 419.890590 46.441680 2.034830 0.000000 \n",
"4 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
" VENDAS_TOTAIS \n",
"0 36513.548652 \n",
"1 130.383601 \n",
"2 26190.520005 \n",
"3 822.148690 \n",
"4 0.000000 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agrotx.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Emissão de carbono"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"geo_data = gpd.GeoDataFrame.from_file(\"files/Carbon_emissions_from_aboveground_biomass_loss.shx\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" <th>link</th>\n",
" <th>name</th>\n",
" <th>objectid</th>\n",
" <th>st_areasha</th>\n",
" <th>st_lengths</th>\n",
" <th>tile_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POLYGON ((5.000138888828192 -3.000138889236719...</td>\n",
" <td>http://s3.amazonaws.com/WHRC-carbon/whrc_emiss...</td>\n",
" <td>00N_000E_merge</td>\n",
" <td>1</td>\n",
" <td>1.859723e+11</td>\n",
" <td>1.781417e+06</td>\n",
" <td>00N_000E_mtc02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>POLYGON ((9.999861111179843 -9.999861111027437...</td>\n",
" <td>http://s3.amazonaws.com/WHRC-carbon/whrc_emiss...</td>\n",
" <td>00N_010E_merge</td>\n",
" <td>2</td>\n",
" <td>1.245542e+12</td>\n",
" <td>4.464169e+06</td>\n",
" <td>00N_010E_mtc02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POLYGON ((19.99986111088579 -9.999861111027437...</td>\n",
" <td>http://s3.amazonaws.com/WHRC-carbon/whrc_emiss...</td>\n",
" <td>00N_020E_merge</td>\n",
" <td>3</td>\n",
" <td>1.245542e+12</td>\n",
" <td>4.464169e+06</td>\n",
" <td>00N_020E_mtc02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>POLYGON ((29.99986111059171 -9.999861111027437...</td>\n",
" <td>http://s3.amazonaws.com/WHRC-carbon/whrc_emiss...</td>\n",
" <td>00N_030E_merge</td>\n",
" <td>4</td>\n",
" <td>1.245542e+12</td>\n",
" <td>4.464169e+06</td>\n",
" <td>00N_030E_mtc02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POLYGON ((39.99986111119595 -9.999861111027437...</td>\n",
" <td>http://s3.amazonaws.com/WHRC-carbon/whrc_emiss...</td>\n",
" <td>00N_040E_merge</td>\n",
" <td>5</td>\n",
" <td>9.964339e+11</td>\n",
" <td>4.018891e+06</td>\n",
" <td>00N_040E_mtc02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry \\\n",
"0 POLYGON ((5.000138888828192 -3.000138889236719... \n",
"1 POLYGON ((9.999861111179843 -9.999861111027437... \n",
"2 POLYGON ((19.99986111088579 -9.999861111027437... \n",
"3 POLYGON ((29.99986111059171 -9.999861111027437... \n",
"4 POLYGON ((39.99986111119595 -9.999861111027437... \n",
"\n",
" link name \\\n",
"0 http://s3.amazonaws.com/WHRC-carbon/whrc_emiss... 00N_000E_merge \n",
"1 http://s3.amazonaws.com/WHRC-carbon/whrc_emiss... 00N_010E_merge \n",
"2 http://s3.amazonaws.com/WHRC-carbon/whrc_emiss... 00N_020E_merge \n",
"3 http://s3.amazonaws.com/WHRC-carbon/whrc_emiss... 00N_030E_merge \n",
"4 http://s3.amazonaws.com/WHRC-carbon/whrc_emiss... 00N_040E_merge \n",
"\n",
" objectid st_areasha st_lengths tile_name \n",
"0 1 1.859723e+11 1.781417e+06 00N_000E_mtc02 \n",
"1 2 1.245542e+12 4.464169e+06 00N_010E_mtc02 \n",
"2 3 1.245542e+12 4.464169e+06 00N_020E_mtc02 \n",
"3 4 1.245542e+12 4.464169e+06 00N_030E_mtc02 \n",
"4 5 9.964339e+11 4.018891e+06 00N_040E_mtc02 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geo_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fabc89ee940>"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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+JaXdv2aj7vRJYGZsq9W6p+/+LDtOrc9W17+yC61///0TleR92fP35twwAduFgroyh192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\nAAASRlAHAAAAAAkjqAMAAACAhP0/UP+JfneUToEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fabc8a41a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"geo_data.plot(column=\"st_areasha\",figsize=(15,8))"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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